MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds.

Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT).

Deep Multi-Scale Feature Learning for Defocus Blur Estimation.

This paper presents an edge-based defocus blur estimation method from a single defocused image. We first distinguish edges that lie at depth discontinuities (called depth edges, for which the blur estimate is ambiguous) from edges that lie at approximately constant depth regions (called pattern edges, for which the blur estimate is well-defined). Then, we estimate the defocus blur amount at pattern edges only, and explore an interpolation scheme based on guided filters that prevents data propagation across the detected depth edges to obtain a dense blur map with well-defined object boundaries.

Edge-Based Defocus Blur Estimation With Adaptive Scale Selection.

Objects that do not lie at the focal distance of a digital camera generate defocused regions in the captured image. This paper presents a new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes. The proposed approach initially computes a scale-consistent edge map of the input image and selects a local reblurring scale aiming to cope with noise, edge mis-localization, and interfering edges.